Content-Based Image Retrieval
31 papers with code • 1 benchmarks • 5 datasets
Content-Based Image Retrieval is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.
Source: Camera Obscurer: Generative Art for Design Inspiration
Benchmarks
These leaderboards are used to track progress in Content-Based Image Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Oxford5k
PS-Battles
GPR1200
European Flood 2013 Dataset
INRIA Holidays Dataset
Latest papers with no code
Texture image retrieval using a classification and contourlet-based features
In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images.
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques
This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques.
Lesion Search with Self-supervised Learning
Content-based image retrieval (CBIR) with self-supervised learning (SSL) accelerates clinicians' interpretation of similar images without manual annotations.
Annotation Cost Efficient Active Learning for Content Based Image Retrieval
Unlike the existing AL methods for CBIR, at each AL iteration of ANNEAL a human expert is asked to annotate the most informative image pairs as similar/dissimilar.
Class Anchor Margin Loss for Content-Based Image Retrieval
The performance of neural networks in content-based image retrieval (CBIR) is highly influenced by the chosen loss (objective) function.
Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems.
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval
Content-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture.
Multimorbidity Content-Based Medical Image Retrieval Using Proxies
Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present.
Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss
Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories.
Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval
An extensive experimental work has been conducted to study the efficiency of using semantic information as well as the bag of visual words model for natural and urban scene image retrieval.
